Example-based translation is essentially translation by analogy. An Example-Based Machine Translation (EBMT) system is given a set of sentences in the source language (from which one is translating) and their corresponding translations in the target language, and uses those examples to translate other, similar source-language sentences into the target language. The basic premise is that, if a previously translated sentence occurs again, the same translation is likely to be correct again.
At the foundation of example-based machine translation is the idea of translation by analogy. When applied to the process of human translation, the idea that translation takes place by analogy is a rejection of the idea that people translate sentences by doing deep linguistic analysis. Instead it is founded on the belief that people translate firstly by decomposing a sentence into certain phrases, then by translating these phrases, and finally by properly composing these fragments into one long sentence. Phrasal translations are translated by analogy to previous translations. The principle of translation by analogy is encoded to example-based machine translation through the example translations that are used to train such a system.
The idea for EBMT dates from about the same time, though the paper presented by Makoto Nagao at a 1981 conference was not published until three years later(Nagao 1984). The essence of EBMT, called “machine translation by exampleguidedinference, or machine translation by the analogy principle”
Example-based machine translation systems are trained from bilingual parallel corpora, which contain sentence pairs like the example shown in the table. Sentence pairs contain sentences in one language with their translations into another. The particular example shows an example of a minimal pair, meaning that the sentences vary by just one element. These sentences make it simple to learn translations of subsentential units. For example, an example-based machine translation system would learn three units of translation: